# -*- coding: utf-8 -*-
"""
@author: YuHaiyang

"""
import urllib

import torch
from PIL import Image
from torchvision import transforms


def print_hi(name):
    # Use a breakpoint in the code line below to debug your script.
    print(f'Hi, {name}')  # Press ⌘F8 to toggle the breakpoint.


# Press the green button in the gutter to run the script.
if __name__ == '__main__':
    model = torch.hub.load('pytorch/vision:v0.10.0', 'alexnet', pretrained=True)
    model.eval()

    url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
    try:
        urllib.URLopener().retrieve(url, filename)
    except:
        urllib.request.urlretrieve(url, filename)

    input_image = Image.open(filename)
    preprocess = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])
    input_tensor = preprocess(input_image)
    input_batch = input_tensor.unsqueeze(0)  # create a mini-batch as expected by the model

    # move the input and model to GPU for speed if available
    if torch.cuda.is_available():
        input_batch = input_batch.to('cuda')
        model.to('cuda')

    with torch.no_grad():
        output = model(input_batch)
    # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
    print(output[0])
    # The output has unnormalized scores. To get probabilities, you can run a softmax on it.
    probabilities = torch.nn.functional.softmax(output[0], dim=0)
    # print(probabilities)